Monitoring the UK’s wild mammals: A new grammar for citizen science engagement and ecology

Anthropogenic activities have imperilled not just global ecosystems, but also the ecosystem services they provide which are crucial for human livelihoods. To understand these changes, there is a need for effective monitoring over large spatial and temporal scales. This thesis will build on two proposed solutions. First, citizen science – defined here as the involvement of non-professionals in scientific enquiry – allows the crowdsourcing of data collection and classification to expand monitoring in ways that are logistically infeasible for ecologists alone. Second, motion-sensing camera traps can reduce the labour needed for monitoring since they can be deployed for long periods and provide continuous, relatively unbiased observations. In this thesis, I describe MammalWeb, a citizen science project in north-east England where I enlisted the aid of the local community in wild mammal monitoring. Motivated by the current unevenness of survey effort and data for mammals in Great Britain, MammalWeb involves citizen scientists in both the collection and classification of camera trap images, a novel combination. This is a multidisciplinary project, and in the following chapters I will begin, in Chapter 2, with a detailed reflection on the organisation of the MammalWeb citizen science project and approaches to evaluating its performance. I observe that the majority of contributions came from a small subset of citizen scientists. In Chapter 3, I develop an economical approach to deriving consensus classifications from the aggregated input of multiple users, which is a crucial part of many citizen science projects. This is followed in Chapter 4 by a case study of a partnership I initiated between MammalWeb and the local Belmont Community School, where we empowered a group of secondary school students to not only aid in collecting data for MammalWeb, but also design and deliver ecological outreach to their community. This is now the template for a wider network of school partnerships we are pursuing. Chapter 5 will examine common concerns around estimating species occupancy from camera trap data, including post-hoc discretisation of observations and effects of missing data. I also develop a resampling method to account for uncertain detections, a common issue when crowdsourcing data classifications. I show that, through resampling, the estimated parameters from occupancy models are robust against high uncertainty in the underlying detections. Lastly, Chapter 6 will discuss how my work on MammalWeb has laid the foundation for a wider citizen science camera trapping network in the United Kingdom and avenues for future work. Importantly, I show that MammalWeb citizen scientists have been empowered to be more than “mobile sensors” and act as independent researchers who have initiated ecological studies elsewhere

Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

Public Participation in Scientific Research: a Framework for Deliberate Design

Members of the public participate in scientific research in many different contexts, stemming from traditions as varied as participatory action research and citizen science. Particularly in conservation and natural resource management contexts, where research often addresses complex social–ecological questions, the emphasis on and nature of this participation can significantly affect both the way that projects are designed and the outcomes that projects achieve. We review and integrate recent work in these and other fields, which has converged such that we propose the term public participation in scientific research (PPSR) to discuss initiatives from diverse fields and traditions. We describe three predominant models of PPSR and call upon case studies suggesting that—regardless of the research context—project outcomes are influenced by (1) the degree of public participation in the research process and (2) the quality of public participation as negotiated during project design. To illustrate relationships between the quality of participation and outcomes, we offer a framework that considers how scientific and public interests are negotiated for project design toward multiple, integrated goals. We suggest that this framework and models, used in tandem, can support deliberate design of PPSR efforts that will enhance their outcomes for scientific research, individual participants, and social–ecological systems

Towards citizen-expert knowledge exchange for biodiversity informatics: A conceptual architecture

This article proposes a conceptual architecture for citizen-expert knowledge exchange in biodiversity management. Expert services, such as taxonomic identification, are required in many biodiversity management activities, yet these services remain inaccessible to poor communities, such as small-scale farmers. The aim of this research was to combine ontology and crowdsourcing technologies to provide taxonomic services to such communities. The study used a design science research (DSR) approach to develop the conceptual architecture. The DSR approach generates knowledge through building and evaluation of novel artefacts. The research instantiated the architecture through the development of a platform for experts and farmers to share knowledge on fruit flies. The platform is intended to support rural fruit farmers in Kenya with control and management of fruit flies. Expert knowledge about fruit flies is captured in an ontology that is integrated into the platform. The non-expert citizen participation includes harnessing crowdsourcing technologies to assist with organism identification. An evaluation of the architecture was done through an experiment of fruit fly identification using the platform. The results showed that the crowds, supported by an ontology of expert knowledge, could identify most samples to species level and in some cases to sub-family level. The conceptual architecture may guide and enable creation of citizen-expert knowledge exchange applications, which may alleviate the taxonomic impediment, as well as allow poor citizens access to expert knowledge. Such a conceptual architecture may also enable the implementation of systems that allow non-experts to participate in sharing of knowledge, thus providing opportunity for the evolution of comprehensive biodiversity knowledge systems.CA2016www.wits.ac.za/linkcentre/aji

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill‐suited for citizen science. As camera trap technology has evolved, cameras have become more user‐friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly‐advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement

Strategic research and innovation agenda on circular economy

CICERONE aims to bring national, regional and local governments together to jointly tackle the circular economy transition needed to reach net-zero carbon emissions and meet the targets set in the Paris Agreement and EU Green Deal. This document represents one of the key outcomes of the project: a Strategic Research & Innovation Agenda (SRIA) for Europe, to support owners and funders of circular economy programmes in aligning priorities and approaching the circular economy transition in a systemic way